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Study On Carbon Emission Characteristics And Carbon Peak Prediction In Our Country Based On Quantitative Identification Of Influencing Factors

Posted on:2024-04-28Degree:MasterType:Thesis
Country:ChinaCandidate:P X HuangFull Text:PDF
GTID:2531307067478344Subject:Urban construction management
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In the context of global low-carbon development,achieving China’s goals of carbon peaking and carbon neutrality has become a focus of attention in academia.There have been numerous studies predicting the trend of carbon emissions and the peak value of future carbon emissions in China.Relevant research mainly focuses on the factors influencing carbon emissions at the energy consumption end and the study of carbon peaking.However,the prediction results of carbon peak value are influenced by the selected prediction methods and factors,leading to significant differences in existing research conclusions,which makes it difficult to provide reference for emission reduction practices.Therefore,this study aims to further improve the process of predicting the peak value of carbon emissions at the energy consumption end in China based on existing research,which will contribute to China’s transformation towards a green and low-carbon country.This study first conducts a preliminary analysis of the factors influencing carbon emissions at the energy consumption end in China through a bibliometric analysis.Based on this analysis,the random forest algorithm and Dematel method are utilized to perform dimensionality reduction analysis on 19 influencing factors,identifying seven major factors affecting carbon emissions at the energy consumption end.Three different scenario models,namely baseline scenario,high-carbon scenario,and low-carbon scenario,are set for these seven factors.The STIRPAT model is then used to predict the peak value of carbon emissions at the energy consumption end in China,and quantitative analysis of each factor is conducted based on the peak value prediction results.From a nationwide perspective,the study analyzes the spatial differences in carbon emissions at the provincial level in China and the characteristics of carbon peaking,providing differentiated suggestions for carbon emission reduction at the energy consumption end in different provinces of China.The research findings show that the carbon emissions at the energy consumption end in China are mainly influenced by seven factors: population,total primary energy production,total energy consumption,urbanization rate,total fixed asset investment in the whole society,GDP,and the proportion of natural gas energy consumption.Under the baseline scenario,China is predicted to achieve carbon peaking in 2040,with a peak value of 14,012.48 Mt.Under the high-carbon scenario,China is expected to achieve carbon peaking in 2045,with a peak value of 15,579.10 Mt.Under the low-carbon scenario,China is projected to achieve carbon peaking in 2035,with a peak value of 12,828.39 Mt.Carbon emissions at the energy consumption end in Chinese provinces show an uneven distribution,and particular attention should be given to carbon emission reduction in Hebei Province,Shanxi Province,Inner Mongolia Autonomous Region,Liaoning Province,Jiangsu Province,Shandong Province,Henan Province,and Guangdong Province.Meanwhile,Hebei Province,Jiangsu Province,and Shandong Province are projected to reach their peak emissions between 2035 and 2045,with the largest impact from population size,followed by energy and economy.This study provides a new perspective for researchers studying the factors influencing carbon emissions at the energy consumption end,expands the content of empirical research on these factors,and enriches the understanding of the relationship between carbon emissions at the energy consumption end and carbon peaking.It also serves as a theoretical basis for subsequent optimization studies and the formulation of long-term carbon reduction targets in China.Moreover,it promotes the application of decarbonization technologies in China and advances the country’s low-carbon development...
Keywords/Search Tags:Random Forest, Dematel method, scenario analysis, peak value of energy consumption end
PDF Full Text Request
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